When the parse forests of the unlabeled data are the union of the outputs of GParser and ZPar, denoted as "Unlabeled ← Z+G", each word has 1.053 candidate heads on English and 1.136 on Chinese, and the oracle accuracy is higher than using 1-best outputs of single parsers (94.97% vs. 92.85% on English, 86.66% vs. 82.46% on Chinese).

we find that although the parser significantly outperforms the supervised GParser on English, it does not gain significant improvement over co-training with ZPar ("Unlabeled ← Z") on both English and Chinese.


This space then reflects the "semantic ground truth" of shared lexical meanings in a language community's vocabulary.

corpus-based VSMs have been criticized as being noisy or incomplete representations of meaning (Glenberg and Robertson, 2000).


1 If brain activation data encodes semantics, we theorized that including brain data in a model of semantics could result in a model more consistent with semantic ground truth.

the inclusion of brain data will only improve a text-based model if brain data contains semantic information not readily available in the corpus.


In either case, one can build a user simulation model that is the average of different user behaviors or learn a policy from a corpus that contains a variety of interaction patterns, and thus safely assume that single-agent RL techniques will work.

in the latter case if the behavior of the user changes significantly over time then the assumption that the environment is stationary will no longer hold.


Q-learning failed to converge in all cases, except for very small state space sizes.

both PHC and PHC-WoLF always converged (or in the case of 7 fruits they needed more training episodes) and performed similarly.


Also, the employment of SVM classifiers allows the incorporation of rich features for better data representation (Feng and Hirst, 2012).

hILDA's approach also has obvious weakness: the greedy algorithm may lead to poor performance due to local optima, and more importantly, the SVM classifiers are not well-suited for solving structural problems due to the difficulty of taking context into account.


2013, we perform a sentence-level parsing for each sentence first, followed by a textlevel parsing to generate a full discourse tree for the whole document.

in addition to efficiency (to be shown in Section 6), our discourse parser has a distinct feature, which is the postediting component (to be introduced in Section 5), as outlined in dashes.


With respect to the macroaveraged F1-scores, adding the post-editing component also obtains about 1% improvement.

the overall MAFS is still at the lower  end of 30% for all constituents.


Negative expressions are common in natural language text and play a critical role in information extraction.

the performances of current systems are far from satisfaction, largely due to its focus on intrasentence information and its failure to consider inter-sentence information.


The research on negation focus identification was pioneered by Blanco and Moldovan (2011), who investigated the negation phenomenon in semantic relations and proposed a supervised learning approach to identify the focus of a negation expression.

although Morante and Blanco (2012) proposed negation focus identification as one of the *SEM'2012 shared tasks, only one team (Rosenberg and Bergler, 2012) 1 participated in this task.


In (Li et al., 2005), new word detection was viewed as a binary classification problem.

these supervised models requires not only heavy engineering of linguistic features, but also expensive annotation of training data.


Comparison between LRT + LP E (or LRT + LP E + N W P ) and LRT shows that inclusion of left pattern entropy also boosts the performance apparently.

the new word probability (N W P ) has only marginal contribution to improvement.


The sentiment captured in opinionated text provides interesting and valuable information for social media services.

due to the complexity and diversity of linguistic representations, it is challenging to build a framework that accurately extracts such sentiment.


Automatically extracting sentiments from usergenerated opinionated text is important in building social media services.

the complexity and diversity of the linguistic representations of sentiments make this problem challenging.


To capture more complex linguistic phenomena, leading approaches (Nakagawa et al., 2010;Jo and Oh, 2011;Kim et al., 2013) apply more advanced models but assume one document or sentence holds one sentiment.


Also, existing approaches infer sentiments without considering the changes of sentiments within or between clauses.

these changes can be successfully exploited for inferring fine-grained sentiments.


Although the word "ho-hum" indicates a negative polarity, it is not a frequent word.

the conjunction "but" clearly signals a contrast.


's approach works on the sub sentential level.

it differs from Re-New in three aspects.


Most of the ORE systems utilize weak supervision knowledge to guide the extracting process, such as: Databases (Craven and Kumlien, 1999), Wikipedia (Wu and Weld, 2007;Hoffmann et al., 2010), Regular expression (Brin, 1999;Agichtein and Gravano, 2000), Ontology (Carlson et al., 2010;Mohamed et al., 2011) or Knowledge Base extracted automatically from Internet (Mintz et al., 2009;Takamatsu et al., 2012).

when iteratively coping with large heterogeneous data, the ORE systems suffer from the "semantic drift" problem, caused by error accumulation (Curran et al., 2007).


The traditional segmentation method may generate four lexical features {'台北', '大安', '森 林', '公园'}, which is a partition of the relation mention.

the Omni-word feature denoting all the possible words in the relation mention may generate features as: Most of these features are nested or overlapped mutually.


The first impression is that more lexicon entries result in more power.

more lexicon entries also increase the computational complexity and bring in noises.


(2010) propose a crosslingual annotation projection approach which uses parallel corpora to acquire a relation detector on the target language.

the mapping of two entities involved in a relation instance may leads to errors.


Both corpora have the same entity/relation hierarchies, which define 7 entity types, 6 major relation types.

the Chinese corpus contains 633 documents and 9,147 positive relation instances while the English corpus only contains 498 files and 6,253 positive instances.


These models support efficient dynamic-programming inference, but only model local dependencies in the output label sequence.

citations have strong global regularities not captured by these models.


Note that some pairs of these constraints are redundant or logically incompatible.

we are using them as soft constraints, so these constraints will not necessarily be satisfied by the output of the model, which eliminates concern over enforcing logically impossible outputs.


Soft constraints can be implemented inefficiently using hard constraints and dual decomposition-by introducing copies of output variables and an auxiliary graphical model, as in .

at every iteration of dual decomposition, MAP must be run in this auxiliary model.


Furthermore the copying of variables doubles the number of iterations needed for information to flow between output variables, and thus slows convergence.

our approach to soft constraints has identical per-iteration complexity as for hard constraints, and is a very easy modification to existing hard constraint code.


Various term weighting strategies have been proposed and studied for the term-based representation (Amati and Van Rijsbergen, 2002;Singhal et al., 1996;Robertson et al., 1996).

existing studies on concept-based representation still used weighting strategies developed for term-based representation such as vector space models (Qi and Laquerre, 2012) and divergence from randomness (DFR) (Limsopatham et al., 2013a) and did not take the inaccurate concept mapping results into consideration.


Under such a situ- ation, traditional retrieval functions would likely work well and generate satisfying retrieval performance since the relations among concepts are independent which is consistent with the assumptions made in traditional IR (Manning et al., 2008).

the mapping results generated by MetaMap are not perfect.


Distributional representations of words have been successfully used in many language processing tasks such as entity set expansion (Pantel et al., 2009), part-of-speech (POS) tagging and chunking (Huang and Yates, 2009), ontology learning (Curran, 2005), computing semantic textual similarity (Besançon et al., 1999), and lexical inference (Kotlerman et al., 2012).

the distribution of a word often varies from one domain 1 to another.


The unsupervised DA setting that we consider does not assume the availability of labeled data for the target domain.

if a small amount of labeled data is available for the target domain, it can be used to further improve the performance of DA tasks (Xiao et al., 2013;Daumé III, 2007).


For representation, we considered distributional features u (i) in descending order of their scores given by Equation 4, and then taking the inverse-rank as the values for the distributional features (Bollegala et al., 2011).

none of these alternatives resulted in performance gains.


The selection of pivots is vital to the performance of SFA.

unlike SFA, which requires us to carefully select a small subset of pivots (ca.


For example, given the vectors representing red and car, composition derives a vector that approximates the meaning of red car.

the link between language and meaning is, obviously, bidirectional: As message recipients we are exposed to a linguistic expression and we must compute its meaning (the synthesis problem).


Andreas and Ghahramani (2013) discuss the the issue of generating language from vectors and present a probabilistic generative model for distributional vectors.

their emphasis is on reversing the generative story in order to derive composed meaning representations from word sequences.


They introduce a bidirectional languageto-meaning model for compositional distributional semantics that is similar in spirit to ours.

we present a clearer decoupling of synthesis and generation and we use different (and simpler) training methods and objective functions.


(2012) reconstruct phrase tables based on phrase similarity scores in semantic space.

they resort to scoring phrase pairs extracted from an aligned parallel corpus, as they do not have a method to freely generate these.


We observe that this model has a very high precision (since many token sequences marked as motifs would recur in similar contexts, and would thus have the same motif boundaries).

the rule-based method has a very row recall due to lack of generalization capabilities.


Ideally we hope to find a permutation of the surrogate weights to map to a tensor in such a way that the tensor has a rank as low as possible.

matrix rank minimization is in general a hard problem (Fazel, 2002).


In fact, we utilize this algorithm in our propagation step ( §2.4).

the former work operates only at the level of sentences, and while the latter does extend the framework to sub-spans of sentences, they do not discover new translation pairs or phrasal probabilities for new pairs at all, but instead re-estimate phrasal probabilities using the graph structure and add this score as an additional feature during decoding.


Some recent work has looked at anaphora resolution (Hardmeier and Federico, 2010) and discourse connectives (Cartoni et al., 2011;Meyer, 2011), to mention two examples.

1 so far the attempts to incorporate discourse-related knowledge in MT have been only moderately successful, at best.


As shown in Figure 2a, DR does not include any lexical item, and therefore measures the similarity between two translations in terms of their discourse structures only.

dR-LEX includes the lexical items to account for lexical matching; moreover, it separates the structure (the skeleton) of the tree from its labels, i.e.


Adding DR and DR-LEX to the combinations manages to improve over five and four of the six tuned ASIYA metrics, respectively.


Like the traditional phrase translation model, the translation score of each bilingual phrase pair is modeled explicitly in our model.

instead of estimating the phrase translation score on aligned parallel data, our model intends to capture the grammatical and semantic similarity between a source phrase and its paired target phrase by projecting them into a common, continuous space that is language independent.


by adding a term of 2 norm) to deal with overfitting.

we did not find clear empirical advantage over the simpler early stop approach in a pilot study, which is adopted in the experiments in this paper.


A common trait of all current approaches, in fact, is the reliance on batch learning techniques, which assume a "static" nature of the world where new unseen instances that will be encountered will be similar to the training data.

4 similarly to translation memories that incrementally store translated segments and evolve over time incorporating users style and terminology, all components of a CAT tool (the MT engine and the mechanisms to assign quality scores to the suggested translations) should take advantage of translators feedback.


If its value is larger than the tolerance parameter ( ), the weights of the model are updated as much as the aggressiveness parameter C allows.

with OSVR, which keeps track of the most important points seen in the past (support vectors), the update of the weights is done without considering the previously processed i-1 instances.


Recent studies (Zhu et al., 2013; show, however, that this approach can also achieve the state-of-the-art performance with improved training procedures and the use of additional source of information as features.

there is still room for improvement for these state-of-the-art transition-based constituent parsers.


For example, in Figure 1, for the input sentence w 0 w 1 w 2 and its POS tags abc, our parser can construct two parse trees using action sequences given below these trees.

parse trees in Treebanks often contain an arbitrary number of branches.


Assuming an input sentence contains n words, in order to reach a terminal state, the initial state requires n sh-x actions to consume all words in β, and n − 1 rl/rr-x actions to construct a complete parse tree by consuming all the subtrees in σ.


One advantage of transition-based constituent parsing is that it is capable of incorporating arbitrarily complex structural features from the already constructed subtrees in σ and unprocessed words in β.

all the feature templates given in Table 1 are just some simple structural features.


Certain verbs sometimes have more than one obligatory preposition as in range from A to B.

the large majority of verbs satisfy rule (b).


In the E step of EM, we compute a probability distribution (according to the current model) over all possible completions of the observed data, and the expected counts of all types, which may be fractional.

note that in each completion of the data, the counts are integral.


(Also note that (6) becomes an exact solution to the marginal constraint.)

theoretically, this requires us to derive a new estimate for D. as this is not trivial, nearly all implementations simply use the original estimate (4).


Then, in the M step, we find the parameter values that maximize their likelihood.

mLE is prone to overfitting, one symptom of which is the "garbage collection" phenomenon where a rare English word is wrongly aligned to many French words.


Our method assembles observed names into an evolutionary tree.

the true tree must include many names that fall outside our small observed corpora, so our model would be a more appropriate fit for a far larger corpus.


This difference might have been caused by the above-mentioned errors.

at least, we can ascertain the important fact that the results for the corpora reduced by 1/100 are not so different from those of the original corpora from the perspective of their perplexity measures.


Kernel-based supervised methods such as dependency tree kernels (Culotta and Sorensen, 2004), subsequence kernels (Bunescu and Mooney, 2006) and convolution tree kernels (Qian et al., 2008) have been rather successful in learning this task.

purely supervised relation extraction methods assume the availability of sufficient labeled data, which may be costly to obtain for new domains.


This is because we have used a lot less labeled instances in the target domains: only 10% are used.

the gaps reduces when the number of source domains increases.


As we observed in a pilot experiment that there is a good chance that the predictions ranked in the second or third may still be correct, we select top three predictions as the candidate relations for each mention in order to introduce more potentially correct output.

we should discard the predictions whose confidences are too low to be true, where we set up a threshold of 0.1.


For example, in Figure 1, given USA as the subject of the relation Capital, we can only accept one possible object, because there is great chance that a country only have one capital.

given Washington D.C. as the object of the relation Capital, we can only accept one subject, since usually a city can only be the capital of one country or state.


All these results show that embedding the relation background information into RE can help eliminate the wrong predictions and improve the results.

in the Riedel's dataset, Mintz++, the MaxEnt relation extractor, does not perform well, and our framework cannot improve its performance.


For example, the fourth relation mention in Figure 1 should have been labeled by the relation Senate-of.

the incomplete knowledge base does not contain the corresponding relation instance (Senate-of(Barack Obama, U.S.)).


Figure 5 demonstrates that the ranks of data matrices are approximately 2,000 for the initial optimization of DRMC-b and DRMC-1.

those high ranks result in poor performance.


Transitional expressions provide glue that holds ideas together in a text and enhance the logical organization, which together help improve readability of a text.

in most current statistical machine translation (SMT) systems, the outputs of compound-complex sentences still lack proper transitional expressions.


Thus, generating transitional expressions is necessary for achieving grammatical cohesion.

it is not easy to produce such transitional expressions in SMT.


Second, the MT quality estimation might be inconsistent across different document-specific MT models, thus the confidence score is unreliable and not very helpful to users.

to traditional static MT quality estimation methods, our approach not only trains the MT quality estimator dynamically for each document-specific MT model to obtain higher prediction accuracy, but also achieves consistency over different document-specific MT models.


Conclusions with regard to context width may have to be tempered somewhat, as the performance of the l1r1 configuration was found to not be significantly better than that of the l2r2 configuration.

l1r1 performs significantly better than l3r3 at p < 0.01, and l2r2 performs significantly better than l3r3 at p < 0.01.


Another discrepancy is found in the BLEU scores of the English→Chinese experiments, where we measure an unexpected drop in BLEU score under baseline.

all other scores do show the expected improvement.


The task is related to exploratory search (Marchionini, 2006).

to classical information seeking, in exploratory search, the user is uncertain about the information available, and aims at learning and understanding a new topic (White and Roth, 2009).


We define Query-Chain Focused Summarization as follows: for each query in an exploratory search session, we aim to extract a summary that answers the information need of the user, in a manner similar to Query-Focused Summarization, while not repeating information already provided in previous steps, in a manner similar to Update Summarization.

to queryfocused summarization, the context of a sum-mary is not a single query, but the set of queries that led to the current step, their result sets and the corresponding summaries.


A summary about the hurricane need not contain all of these sentences as they are all describing the same thing.

it is not trivial for the lexically-motivated MMR algorithm to detect that events like "passed", "uprooted" or "damaged" are in fact repetitive.


At these higher thresholds, temporal information is still able to help get an improvement in R-2.

as this affects only very few out of the 44 document sets, statistical variances mean that these R-2 scores are no longer  = = (R4) Muntadhar al-Zaidi, reporter of Baghdadiya television jumped and threw his two shoes one by one at the president, who ducked and thus narrowly missed being struck, raising chaos in the hall in Baghdad's heavily fortified green Zone.


1 It is a truth universally acknowledged that an annotation task in good standing be in possession of a measure of inter-annotator agreement (IAA).

no such measure is in widespread use for the task of syntactic annotation.


For example the only difference between the two leftmost trees in Figure 2 is a modifier, but δ plain gives them distance 4 and δ dif f 0.

δ dif f might underestimate some distances as well; for exam-ple the leftmost and rightmost trees also have distance zero using δ dif f , despite our syntactic intuition that the difference between a transitive and an intransitive should be taken account of.


The noun involved in the copula relation is actress and thus it is taken as the page's hypernym lemma.

the extracted hypernym is sometimes overgeneral (one, kind, type, etc.).


For example, consider the category FRENCH TELEVI-SION PEOPLE; since this category has no associated pages, in phase 2 no hypernym could be found.

by applying the sub-categories heuristic, we discover that TELEVISION PEOPLE BY COUNTRY is the hypernym most voted by our target category's descendants, such as FRENCH TELEVISION ACTORS and FRENCH TELEVISION DIRECTORS.


A second project, MENTA (de Melo and Weikum, 2010), creates one of the largest multilingual lexical knowledge bases by interconnecting more than 13M articles in 271 languages.

to our work, hypernym extraction is supervised in that decisions are made on the basis of labelled training examples and requires a reconciliation step owing to the heterogeneous nature of the hypernyms, something that we only do for categories, due to their noisy network.


We decided to include the latter for comparison purposes, as it uses knowledge from 271 Wikipedias to build the final taxonomy.

we recognize its performance might be relatively higher on a 2012 dump.


(2013) actually reported accuracy on this dataset.

since their system predicted answers for almost every question (p.c.


The body of work on factoid QA is too broad to be discussed here (see, e.g., the TREC workshops for an overview).


Model features associated with Elaboration relations are ranked highly by the learned model.

the answer preferred by the baseline contains mostly Joint relations,  which "represent the lack of a rhetorical relation between the two nuclei" (Mann and Thompson, 1988) and have very small weights in the model.


The open-domain YA model learns to place more weight on LS features, which are unable to provide the same utility in the biology domain.

so far, we have treated Ls and discourse as distinct features in the reranking model, given that Ls features greatly improve the CR baseline, we hypothesize that a natural extension 16 The interpolation parameter was tuned on the YA development corpus.


(2011) generated semantic knowledge like causality that is written in no sentence.

their method cannot combine more than two pieces of knowledge unlike ours, and their target knowledge consists of nouns, but ours consists of verb phrases, which are more informative.


(2012) extracted 500,000 event causalities with about 70% precision.

as described in Section 1, our event causality criteria are different; since they regarded phrase pairs that were not self-contained as event causality (their annotators checked the original sentences of phrase pairs to see if they were event causality), their judgments tended to be more lenient than ours, which explains the performance difference.


Alternatively, a ranking ap-proach, similar to the one used to generate intranarrative temporal ordering, can also be extended to the cross-narrative case.

the features related to narrative structure and relative and implicit temporal expressions used for temporal ordering within a clinical narrative may not be applicable across narratives.


Sequence alignment algorithms have been developed and popularly used in bioinformatics.

multiple sequence alignment (MSA) has been shown to be NP complete (Wang and Jiang, 1994) and various heuristic algorithms have been proposed to solve this problem (Notredame, 2002).


These projects have produced knowledge bases containing many millions of relational facts between entities.

despite these impressive advances, there are still major limitations regarding precision.


Additionally, in order to ensure that fact candidates mentioned in similar sources have similar believability scores, our believability computation model incorporates influence of comentions.

we must avoid falsely boosting co-mentioned fact candidates.


For example, the triples: Obama born in Kenya and Obama graduated from Harvard are valid fact candidates.

the triple: Obama deserves Nobel Peace Prize is not.


This is because the cardinality of person died in location is one (1).

the cardinality of "INVERSE-OF(died in)" is many(n).


S is subjective, expressing the opinion of the author.

o is objective, stating only what has been alleged.


A summary of the outcome of the study is shown in Figure 1; 74% of the untrustworthy articles were independently labeled as subjective.

64% of trustworthy articles were independently labeled as objective.


In the first version of our model, we could simply set the score of each edge to be w•f (x i , x j ), and the MST recovered in this way would indeed be the highest scoring tree: arg max y P (y|x).

this straightforward approach doesn't apply to the full model which also uses sibling features.


Within a specific domain, terms typically just have a single sense.

our algorithms could certainly be adapted to the case of multiple term senses (by treating the different senses as unique nodes in the tree) in future work.


Direct indicators of hypernymy, such as Hearst-style context patterns, are the core feature for the model and are discovered automatically via discriminative training.

other indicators, such as coordination cues, can indicate that two words might be siblings, independently of what their shared parent might be.


The determiner (w 2 ) and the direct object (w 3 ) are correlated in that the choice of determiner depends on the plurality of w 3 .

the choice of verb (w 1 ) is mostly independent of the determiner.


As we discussed in §3.1 all elements of the distance matrix are functions of observable quantities if the underlying tree u is known.

only the word-word sub-block D W W can be directly estimated from the data without knowledge of the tree structure.


6 NP-hard (Desper and Gascuel, 2005) if u is allowed to be an arbitrary undirected tree.

if we restrict u to be in U, as we do in the above, then maximizingĉ(u) over U can be solved using the bilexical parsing algorithm from Eisner and Satta (1999).


We assume that child learners are able to infer a representation of the situational context from their non-linguistic environment.

in our simulations we approximate the environmental information by running a topic model (Blei et al., 2003) over a corpus of childdirected speech to infer a topic distribution for each situation.


Infants attend to distributional characteristics of their input (Maye et al., 2002(Maye et al., , 2008, leading to the hypothesis that phonetic categories could be acquired on the basis of bottom-up distributional learning alone (de Boer and Kuhl, 2003;Vallabha et al., 2007;McMurray et al., 2009).

this would require sound categories to be well separated, which often is not the case-for example, see Figure 1, which shows the English vowel space that is the focus of this paper.


The third factor, the likelihood of the vowel formants w hi in the categories given by the lexeme v l , is of the same form as the likelihood of vowel categories when resampling lexeme vowel assignments.

here it is calculated over the set of vowels in the token assigned to each vowel category (i.e., the vowels at indices where v t• = c).


Non-adjacent constraints are difficult for string-based approaches because of the exponential number of possible relationships across non-adjacent segments.

the Wolof results show that by learning violations directly, IBPOT does not encounter problems with non-adjacent constraints.


In recent years, there have been an increasing number of studies (Su et al., 2007;Kittur et al., 2008;Sheng et al., 2008;Snow et al., 2008;Callison-Burch, 2009) using crowdsourcing for data annotation.

because annotators that are recruited this way may lack expertise and motivation, the annotations tend to be more noisy and unreliable, which significantly reduces the performance of the classification model.


(2012) propose an algorithm which first trains individual SVM classifiers on several small, class-balanced, random subsets of the dataset, and then reclassifies each training instance using a majority vote of these individual classifiers.

the automatic correction may introduce new noise to the dataset by mistakenly changing a correct label to a wrong one.


Existing approaches toward bias detection have not gone far beyond "bag of words" classifiers, thus ignoring richer linguistic context of this kind and often operating at the level of whole documents.

recent work in sentiment analysis has used deep learning to discover compositional effects (Socher et al., 2011b;Socher et al., 2013b).


The strong correlation between US political parties and political ideologies (Democrats with liberal, Republicans with conservative) lends confidence that this dataset contains a rich mix of ideological statements.

the raw Convote dataset contains a low percentage of sentences with explicit ideological bias.


In Figure 5D, "be used as an instrument to achieve charitable or social ends" reflects a liberal ideology, which the model predicts correctly.

our model is unable to detect the polarity switch when this phrase is negated with "should not".


Experiments on Chinese-English translation show that the reordering approach can significantly improve a state-of-the-art hierarchical phrase-based translation system.

the gain achieved by the semantic reordering model is limited in the presence of the syntactic reordering model, and we therefore provide a detailed analysis of the behavior differences between the two.


The popular distortion or lexicalized reordering models in phrase-based SMT make good local predictions by focusing on reordering on word level, while the synchronous context free grammars in hierarchical phrase-based (HPB) translation models are capable of handling non-local reordering on the translation phrase level.

reordering, especially without any help of external knowledge, remains a great challenge because an accurate reordering is usually beyond these word level or translation phrase level reordering models' ability.


Their linear classifier achieved a reported score of 39.06 2 when combining information from both translators and editors.

our proposed graph-based ranking framework achieves a score of 41.43 when using the same information.


The motivation for using lower order models is that shorter contexts may be observed more often and, thus, suffer less from data sparsity.

a single rare word towards the end of the local context will always cause the context to be observed rarely in the training data and hence will lead to an unreliable estimation.


It also confirms that our motivation to produce lower order n-grams by omitting not only the first word of the local context but systematically all words has been fruitful.

we also see that for the observed sequences the GLM performs slightly worse than MKN.


The idea behind copula theory is that the cumulative distribution function (CDF) of a random vector can be represented in the form of uniform marginal cumulative distribution functions, and a copula that connects these marginal CDFs, which describes the correlations among the input random variables.

in order to have a valid multivariate distribution function regardless of n-dimensional covariates, not every function can be used as a copula function.


Spearman's correlation (Hogg and Craig, 1994) and Kendall's tau (Kendall, 1938) have been widely used in many regression problems in NLP (Albrecht and Hwa, 2007;Yogatama et al., 2011;, and here we use them to measure the quality of predicted valuesŷ by comparing to the vector of ground truth y.

to Pearson's correlation, Spearman's correlation has no assumptions on the relationship of the two measured variables.


The benefit of a semiparametric model is that here we are not interested in performing completely nonparametric estimations, where the infinite dimensional parameters might bring intractability.

by considering the semiparametric case, we not only obtain some expressiveness from the nonparametric models, but also reduce the complexity of the task: we are only interested in the finite-dimensional components Σ in the Gaussian copula with O(n log n) complexity, which is not as computationally difficult as the completely nonparametric cases.


Topic models, an unsupervised technique for inferring translation domains improve machine translation quality.

previous work uses only the source language and completely ignores the target language, which can disambiguate domains.


As we review in Section 2, topic models are a promising solution for automatically discovering domains in machine translation corpora.

past work either relies solely on monolingual source-side models (Eidelman et al., 2012;Hasler et al., 2012;Su et al., 2012), or limited modeling of the target side (Xiao et al., 2012).


(2011) combine these approaches by directly optimizing genre and collection features by computing separate translation tables for each domain.

these approaches treat domains as hand-labeled, constant, and known a priori.


These models take advantage of word or document alignment information and infer more robust topics from the aligned dataset.

lexical information can induce topics from multilingual corpora.


Document-level Alignments Lexical resources connect languages and help guide the topics.

these resources are sometimes brittle and may not cover the whole vocabulary.


Generative Process As in LDA, each word token is associated with a topic.

tree-based topic models introduce an additional step of selecting a concept in a topic responsible for generating each word token.


With 1.6M NIST training sentences, gibbs takes nearly a week to run 1000 iterations.

the parallelized variational and variational-hybrid approaches, which we implement in MapReduce (Dean and Ghemawat, 2004;Wolfe et al., 2008;Zhai et al., 2012), take less than a day to converge.


Each tool is typically trained on hand-annotated data, thus placing SRL at the end of a very highresource NLP pipeline.

richly annotated data such as that provided in parsing treebanks is expensive to produce, and may be tied to specific domains (e.g., newswire).


Some work has also attempted to automatically derive logical meaning representations directly from syntactic CCG parses (Bos, 2005;Lewis and Steedman, 2013).

these approaches to semantics do not ground the text to beliefs in a knowledge base.


The output of their model is a list of hypernyms for a given enity (left panel, Figure 1).

there usually also exists hypernym-hyponym relations among these hypernyms.


Lenci and Benotto (2012) propose another measure focusing on the contexts that hypernyms do not share with their hyponyms.

broader semantics may not always infer broader contexts.


(2013) was unable to scale to long sentences and was only tested on the relatively short sentences in the Microsoft video description corpus used for STS (Agirre et al., 2012).

inference in PSL reduces to a linear programming problem, which is theoretically and practically much more efficient.


For example, for T: "A man is driving" and H: "A man is driving a car", if we use the standard PSL formula for conjunction, the output value is zero because there is no evidence for a car and max(0, X + 0 − 1) = 0 for any truth value 0 ≤ X ≤ 1.

humans find these sentences to be quite similar.


p(x) ∧ q(x) → t() and only one piece of evidence p(C) there are no relevant groundings because there is no evidence for q(C), and therefore, for normal PSL, I(p(C) ∧ q(C)) = 0 which does not affect I(t()).

when using averaging with the same evidence, we need to generate the grounding p(C)∧q(C) because I(p(C)∧q(C)) = 0.5 which does affect I(t()).


One way to solve this problem is to eliminate lazy grounding and generate all possible groundings.

this produces an intractably large network.


For the new nodes or unconnected nodes, we draw an edge with a weight of 1.

when two already connected nodes are added (merged), the weight of their connection is increased by 1.


A word graph, as described above, may contain many sequences connecting start and end.

it is likely that most of the paths are not readable.


Considering this marginal improvement and relatively high results of pure extractive systems, we can infer that the Biased LexRank extracted summaries do not carry much query relevant content.

the significant improvement of our model over the extractive methods demonstrates the success of our approach in presenting the query related content in generated abstracts.


The chat dataset results demonstrate the highest scores: 73% of the sentences generated by our phrasal query abstraction model are grammatically correct and 24% of the generated sentences are almost correct with only one grammatical error, while only 3% of the abstract sentences are grammatically incorrect.

the results varies moving to other datasets.


There is evidently a mismatch between the rules and the test-set; the content selection rules are based on heuristics provided by a L&T Expert rather than by the same pool of lecturers that created the test-set.

the RL is trained to optimise the selected content and not to replicate the existing lecturer summaries, hence there is a difference in accuracy.


In our study, the human ratings correlate well to the average scores achieved by the reward function.

the human ratings do not correlate well to the accuracy scores.


In addition, they serve to establish connectivity for the dependency structure z since commodity can only originate in one location-at the pseudo-token ROOT which has no incoming commodity variables.

in order to enforce these properties on the output dependency structure, this acyclic, connected commodity structure must constrain the activation of the z variables.


For example, consider a typical comment on a YouTube review video about a Motorola Xoom tablet: this guy really puts a negative spin on this , and I 'm not sure why , this seems crazy fast , and I 'm not entirely sure why his pinch to zoom his laggy all the other xoom reviews The comment contains a product name xoom and some negative expressions, thus, a bag-of-words model would derive a negative polarity for this product.

the opinion towards the product is neutral as the negative sentiment is expressed towards the video.


This would strongly bias the FVEC sentiment classifier to assign a positive label to the comment.

the STRUCT model relies on the fact that the negative word, destroy, refers to the PRODUCT (xoom) since they form a verbal phase (VP).


We conjecture that sentiment prediction for AUTO category is largely driven by one-shot phrases and statements where it is hard to improve upon the bag-of-words and sentiment lexicon features.

comments from TABLETS category tend to be more elaborated and well-argumented, thus, benefiting from the expressiveness of the structural representations.


Many existing approaches have a separate, heuristic module for extracting candidate keyphrases prior to keyphrase ranking/extraction.

tomokiyo and Hurst (2003) propose an approach (henceforth LMA) that combines these two steps.


As discussed before, the relationship between two candidates is traditionally established using co-occurrence information.

using cooccurrence windows has its shortcomings.


Most notably, Tratz (2011) achieved a result of 88.4 percent accuracy and Srikumar and Roth (2013) achieved a similar result.

litkowski (2013b) showed that these results did not extend to other corpora, concluding that the FrameNet-based corpus may not have been representative, with a reduction of accuracy to 39.4 percent using a corpus developed by Oxford.


Using PDEP, we found that FrameNet feature values for the governor accounted for 264 of these instances (95 percent), all of which were related to the frame elements Contents or Stuff.

in the TPP corpus, only 3 out of 750 instances were identified for this sense (0.4 percent).


Since dictionary publishers have not previously devoted much effort in analyzing preposition behavior, we believe PDEP may serve an important role, particularly for various NLP applications in which semantic role labeling is important.

pDEp as described in this paper is only in its initial stages.


The main work in bilingual lexicon extraction from comparable corpora is based on the implicit hypothesis that corpora are balanced.

the historical contextbased projection method dedicated to this task is relatively insensitive to the sizes of each part of the comparable corpus.


While prior efforts in NLP have incorporated games for performing annotation and validation (Siorpaes and Hepp, 2008b;Herdagdelen and Baroni, 2012;Poesio et al., 2013), these games have largely been text-based, adding game-like features such as high-scores on top of an existing annotation task.

we introduce two video games with graphical 2D gameplay that is similar to what game players are familiar with.


First, we demonstrate effective video gamebased methods for both validating and extending semantic networks, using two games that operate on complementary sources of information: semantic relations and sense-image mappings.

to previous work, the annotation quality is determined in a fully automatic way.


While the computer can potentially act as a second player, such a simulated player is often limited to using preexisting knowledge or responses, which makes it difficult to validate new types of entities or create novel answers.

we drop this requirement thanks to a new strategy for assigning confidence scores to the annotations based on negative associations.


A further analysis revealed differences in the annotators' thresholds for determining association, with one annotator permitting more abstract relations.

the adjudication process resolved these disputes, resulting in substantial agreement by all annotators on the final gold annotations.


One of the ten questions in a task used an item from N c , resulting in a task mixture of 90% annotation questions and 10% quality-check questions.

we note that both of our video games use data that is 50% annotation, 50% quality-check.


The paid and free versions of TKT had similar numbers of players, while the paid version of Infection attracted nearly twice the players compared to the free version, shown in Table 1, Column 1.

both versions created approximately the same number of annotations, shown in Column 2.


Second, the type of incentive did not change the percentage of items from N that players correctly reject, shown for all players as N -accuracy in Table 1 Column 3 and per-player in Figure 3.

players were much more accurate at rejecting items from N in TKT than in Infection.


The images used by TKT provide concrete examples of a concept, which can be easily compared with the game's current concept; in addition, TKT allows players to inspect items as long as a player prefers.

concept-concept associations require more background knowledge to determine if a relation exists; furthermore, Infection gives players limited time to decide (due to board length) and also contains cognitive distractors (zombies).


3 For images, crowdsourcing workers have a higher IAA than game players; however, this increased agreement is due to adversarial workers consistently selecting the same, incorrect answer.

both video games contain mechanisms for limiting such behavior.


Our current work is inspired by the shallow analysis-based approach of Yoon and Bhat 2012and operates under the same assumptions of capturing the range and sophistication of grammatical constructions at each score level.

the approaches differ in the way in which a spoken response is assigned to a score group.


Indeed there is work in the literature that shows that various topic models, latent or otherwise, can be useful for improving lan-guage model perplexity and word error rate (Khudanpur and Wu, 1999;Chen, 2009;Naptali et al., 2012).

given the preponderance of highly frequent non-content words in the computation of a corpus' WER, it's not clear that a 1-2% improvement in WER would translate into an improvement in term detection.


Work by Wei and Croft (2006) and Chen (2009) take a language model-based approach to  information retrieval, and again, interpolate latent topic models with N-grams to improve retrieval performance.

in many text retrieval tasks, queries are often tens or hundreds of words in length rather than short spoken phrases.


Yet, visually, the relationship in Figure 3a is clearly not linear.

the AP English data exhibits a correlation of ρ = 0.93 (Church and Gale, 1999).


We would discount the adaptation factor when DF w is low and we are unsure of   This approach shows a significant improvement (0.7% absolute) over the baseline.

considering this estimate in light of the two classes of words in Figure 5, there are clearly words in Class B with high burstiness that will be ignored by trying to compensate for the high adaptation variability in the low-frequency range.


Several supervised dependency parsing algorithms (Nivre and Scholz, 2004;McDonald et al., 2005a;McDonald et al., 2005b;McDonald and Pereira, 2006;Carreras, 2007;Koo and Collins, 2010;Ma and Zhao, 2012; have been proposed and achieved high parsing accuracies on several treebanks, due in large part to the availability of dependency treebanks in a number of languages (McDonald et al., 2013).

the manually annotated treebanks that these parsers rely on are highly expensive to create, in particular when we want to build treebanks for resource-poor languages.


the log-likelihood) is given by: Maximum likelihood training chooses parameters such that the log-likelihood L(λ) is maximized.

in our scenario we have no labeled training data for target languages but we have some parallel and unlabeled data plus an English dependency parser.


The prior works showed that these models help to find some segmentations tailored for SMT, since the bilingual word occurrence feature can be captured by the character-based alignment (Och and Ney, 2003).

these models tend to miss out other linguistic segmentation patterns as monolingual supervised models, and suffer from the negative effects of erroneously alignments to word segmentation.


This does not include the cost of n-gram creation or cached lookups, which amount to ∼0.03 seconds per source word in our current implementation.

14 the n-grams created for the NNJM can be shared with the Kneser-Ney LM, which reduces the cost of that feature.


Le's model does obtain an impressive +1.7 BLEU gain on top of a baseline without an NNLM (25.8 vs. 27.5).

when compared to the strongest baseline which includes an NNLM, Le's best models (S2T + T2S) only obtain an +0.6 BLEU improvement (26.9 vs. 27.5).


While this method learns to map word combinations into vectors, it builds on existing word-level vector representations.

we represent words as vectors in a manner that is directly optimized for parsing.


The objective as stated is not jointly convex with respect to U , V and W due to our explicit representation of the low-rank tensor.

if we fix any two sets of parameters, for example, if we fix V and W , then the combined score S γ (x, y) will be a linear function of both θ and U .


In reality other translations might also be acceptable (e.g., both street and road for Straße).

tS1000 accepts more than one correct translation.


12, in which the edge type of each step is important.

this will increase the memory needed for calculation.


Irrespective of their relatively high performance on various semantic tasks, it is debatable whether models that have no access to visual and perceptual information can capture the holistic, grounded knowledge that humans have about concepts.

a possibly even more serious pitfall of vector models is lack of reference: natural language is, fundamentally, a means to communicate, and thus our words must be able to refer to objects, properties and events in the outside world (Abbott, 2010).


However, from a cognitive angle, it relies on strong, unrealistic assumptions: The learner is asked to establish a link between a new object and a word for which they possess a full-fledged textbased vector extracted from a billion-word corpus.

the first time a learner is exposed to a new object, the linguistic information available is likely also very limited.


Surprisingly, the very simple lin method outperforms both CCA and SVD.

nn, an architecture that can capture more complex, non-linear relations in features across modalities, emerges as the best performing model, confirming on a larger scale the recent findings of Socher et al.


Moreover, once the learner observes a new object, she can easily construct a full visual representation for it (and the acquisition literature has shown that humans are wired for good object segmentation and recognition (Spelke, 1994)) -the more challenging task is to scan the ongoing and very ambiguous linguistic communication for contexts that might be relevant and informative about the new object.

fast mapping is often described in the psychological literature as the opposite task: The learner is exposed to a new word in context and has to search for the right object referring to it.


Many existing paraphrase models introduce latent variables to describe the derivation of c from x, e.g., with transformations (Heilman and Smith, 2010;Stern and Dagan, 2011) or alignments (Haghighi et al., 2005;Das and Smith, 2009;Chang et al., 2010).

we opt for a simpler paraphrase model without latent variables in the interest of efficiency.


The lexicon allows for effective parsing, contributing to only 2% of the overall errors.


(2013) trained their model from word alignments produced by traditional unsupervised probabilistic models.

with this approach, errors induced by probabilistic models are learned as correct alignments; thus, generalization capabilities are limited.


Word embedding x t is integrated as new input information in recurrent neural networks for each prediction, but in recursive neural networks, no additional input information is used except the two representation vectors of the child nodes.

some global information , which cannot be generated by the child representations, is crucial for SMT performance, such as language model score and distortion model score.


The first uses a logistic regression model that primarily incorporates high level information about threads and posts.

forum threads have structure which is not leveraged our initial model.


The daily life of Chinese people heavily depends on Chinese input method engine (IME), no matter whether one is composing an E-mail, writing an article, or sending a text message.

every Chinese word inputted into computer or cellphone cannot be typed through one-to-one mapping of key-to-letter inputting directly, but has to go through an IME as there are thousands of Chinese characters for inputting while only 26 letter keys are available in the keyboard.


A first approach to solving smart selection is to select an entity, noun phrase, or concept that subsumes the user selection.

no single approach alone can cover the entire smart selection problem.


We would then find perhaps that Southern California is a more reasonable smart selection than of Southern California.

precisely defining such a relevance function and designing the guidelines for a user study is non-trivial and left for future work.


As we will see below, S1, S2, and S3 are error metrics, so lower scores imply better performance.

p C is a correlation metric, so higher correlation implies better performance.


Note that each of the c i values can be tuned independently because a c i value that is optimal for predicting scores for p i essays with respect to any of the error performance measures is necessarily also the optimal c i when measuring that error on essays from all prompts.

this is not case with Pearson's correlation coefficient, as the P C value for essays from all 13 prompts cannot be simplified as a weighted sum of the P C values obtained on each individual prompt.


In order to obtain an optimal result as measured by P C, we jointly tune the c i parameters to optimize the P C value achieved by our system on the same held-out validation data.

an exact solution to this optimization problem is computationally expensive, as there are too many (7 13 ) possible combinations of c values to exhaustively search.


The training and development sets were completely in full to task participants.

we were unable to download all the training and development sets because some tweets were deleted or not available due to modified authorization status.


The first relies on the judgements of human annotators Mukherjee et al., 2012).

recent studies show that deceptive opinion spam is not easily identified by human readers (Ott et al., 2011).


One contribution of the work presented here is the creation of the cross-domain (i.e., Hotel, Restaurant and Doctor) gold-standard dataset.

to existing work (Ott et al., 2011;Li et al., 2013b), our new gold standard includes three types of reviews: domain expert deceptive opinion spam (Employee), crowdsourced deceptive opinion spam (Turker), and truthful Customer reviews (Customer).


A similar pattern can also be observed when comparing Figure 2 First-Person Singular Pronouns: The literature also associates deception with decreased usage of first-person singular pronouns, an effect attributed to psychological distancing, whereby deceivers talk less about themselves due either to a lack of personal experience, or to detach themselves from the lie (Newman et al., 2003;Zhou et al., 2004;Knapp and Comaden, 1979).

according to our findings, we find the opposite to hold.


By soliciting fake reviews from participants, including crowd workers and domain experts, we have found that is possible to detect fake reviews with above-chance accuracy, and have used our models to explore several psychological theories of deception.

it is still very difficult to estimate the practical impact of such methods, as it is very challenging to obtain gold-standard data in the real world.


The bottom-up hypothesis holds that infants converge on the linguistic units of their language through a similaritybased distributional analysis of their input (Maye et al., 2002;Vallabha et al., 2007).

the top-down hypothesis emphasizes the role of higher level linguistic structures in order to learn the lower level units (Feldman et al., 2013;Martin et al., 2013).


The lexical cue assumes that a pair of words differing in the first or last segment (like [kanaX] and [kanaK]) is more likely to be the result of a phonological process triggered by adjacent sounds, than a true semantic minimal pair.

this strategy clearly gives rise to false alarms in the (albeit relatively rare) case of true minimal pairs like [kanaX] ("duck") and [kanal] ("canal"), where ([X], [l]) will be mistakenly labeled as allophonic.


This fact suggests that a dialog system should be also capable of conducting multi-topic conversations with users to provide them a more natural interaction with the system.

the majority of previous work on dialog interfaces has focused on dealing with only a single target task.


We have not tested the correctness of this variation in the scoring package used for the i2b2 shared task.

it turns out that the CEAF metric (Luo, 2005) was always intended to work seamlessly on predicted mentions, and so has been the case with the B 3 metric.


We found that the overall system ranking remained largely unchanged for both shared tasks, except for some of the lower ranking systems that changed one or two places.

there was a considerable drop in the magnitude of all B 3 scores owing to the combination of two things: (i) mention manipulation, as proposed by Cai and Strube (2010), adds singletons to account for twinless mentions; and (ii) the B 3 metric allows an entity to be used more than once as pointed out by Luo (2005).


It may be thought that inter-annotator agreement (IAA) provides implicit annotation: the higher the agreement, the easier the piece of text is for sentiment annotation.

in case of multiple expert annotators, this agreement is expected to be high for most sentences, due to the expertise.


For example, all five annotators agree with the label for 60% sentences in our data set.

the duration for these sentences has a mean of 0.38 seconds and a standard deviation of 0.27 seconds.


The sentence "it is messy , uncouth , incomprehensible , vicious and absurd" has a SAC of 3.3.

the SAC for the sarcastic sentence "it's like an all-star salute to disney's cheesy commercialism."


Of course the risk in approaching the problem as domain adaptation is that the domains are so different that the representation of a positive instance of a movie or product review, for example, will not coincide with that of a posi-tive scientific citation.

because there is a limited amount of annotated citation data available, by leveraging large amounts of annotated polarity data we could potentially even improve citation classification.


We would like to do as little feature engineering as possible to ensure that the features we use are meaningful across domains.

we do still want features that somehow capture the inherent positivity or negativity of our labeled instances, i.e., citations or Amazon product reviews.


Semi-supervised text classification algorithms proposed in (Nigam et al., 2000), (Joachims, 1999), (Zhu and Ghahramani, 2002) and (Blum and Mitchell, 1998) are a few examples of this type.

these algorithms are sensitive to initial labeled documents and hyper-parameters of the algorithm.


first topic in Table 3), then it may affect the performance of the resulting classifier.

in our approach the annotator can assign multiple class labels to a topic, hence our approach is more flexible for the annotator to encode her domain knowledge efficiently.


(2012) and Khashabi (2013) use pre-trained word embeddings as input for Matrix-Vector Recursive Neural Networks (MV-RNN) to learn compositional structures for RE.

none of these works evaluate word embeddings for domain adaptation of RE which is our main focus in this paper.


In terms of word embeddings for DA, recently, Xiao and Guo (2013) present a log-bilinear language adaptation framework for sequential labeling tasks.

these methods assume some labeled data in target domains and are thus not applicable in our setting of unsupervised DA.


The result suggests that word embeddings seem to capture different information from word clusters and their combination would be effective to generalize relation extractors across domains.

in domain cts, the improvement that word embeddings provide for word clusters is modest.


It may seem that the value of DPK is strictly in its ability to evaluate all paths, which is not explicitly accounted for by other kernels.

another view of the DPK is possible by thinking of it as cheaply calculating rule production similarity by taking advantage of relatively strict English word ordering.


The 'TargetedOnly' scores are somewhat closer to correct, with 12% and 28% of entities with incorrect final polarities.

the 'TargetedOnly' method naturally suffers from a very low recall, with only 19% and 38% of entities covered in the financial and medical domains, respectively.


Given a list of known cognates, our approach does not require any other linguistic information.

it can be customized to integrate historical information regarding language evolution.


Turkish sentences have an unmarked SOV order.

depending on the discourse, constituents can be scrambled to emphasize, topicalize and focus certain elements.


Indeed, it is very easy to construct pairs of translated sentences which involve operations outside our restricted set when transformed into each other.

we use the following method to alleviate the restrictions of the small set of operations.


The present phrasebased setting is simpler because sentences are constructed from left to right, so prefix information is unnecessary.

phrase-based translation implements reordering by allowing hypotheses that translate discontiguous words in the source sentence.


A main contribution in this paper is efficiently ignoring coverage when evaluating the language model.

syntactic machine translation hypotheses correspond to contiguous spans in the source sentence, so in prior work we simply ran the search algorithm in every span.


Continuing the example from Figure 2, we could split ( , ) into three boundary pairs: (country, ), (nations, ), and (countries, ).

it is somewhat inefficient to separately consider the low-scoring child (countries, ).


On one hand, there have been multiple reports (mainly from groups with a long history of building T2S systems) stating that systems using source-side syntax greatly out-perform phrasebased systems (Mi et al., 2008;Zhang et al., 2011;Tamura et al., 2013).

there have been also been multiple reports noting the exact opposite result that sourceside syntax systems perform worse than Hiero, S2T, PBMT, or PBMT with pre-ordering (Ambati and Lavie, 2008;Xie et al., 2011;Kaljahi et al., 2012).


As a result of our tests on English-Japanese (enja) and Japanese-English (ja-en) machine translation, we find that a T2S system not considering these elements performs only slightly better than a standard PBMT system.

after accounting for all these elements we see large increases of accuracy, with the final system greatly exceeding not only standard PBMT, but also state-of-the-art methods based on syntactic pre-or post-ordering.


The most standard search algorithm for T2S translation is bottom-up beam search using cube pruning (CP, Chiang (2007)).

there are a number of other search algorithms that have been proposed for tree-based translation in general (Huang and Chiang, 2007) or T2S systems in particular (Huang and Mi, 2010;Feng et al., 2012).


They created a pre-ordering rule set for dependency parsers from English to several SOV languages.

our rule set is for Chinese-English PBSMT.


Both of our dependency systems outperformed WR07 slightly but were not significant at p = 0.05.

both of them substantially decreased the total times about 60% (or 1,600,000) for pre-ordering rule applications on the training set, compared with WR07.


In our opinion, the reason for the great decrease was that the dependency parse trees were more concise than the constituent parse trees in describing sentences and they could also describe the reordering at the sentence level in a finer way.

the constituent parse trees were more redundant and they needed more nodes to conduct long-distance reordering.


Language-Independent In this work, we integrate only language-independent features, and therefore do not consider morphological or linguistic features.

we apply the model to correct errors in Egyptian Arabic dialect text, following a conventional orthography standard, CODA (Habash et al., 2012).


It is worth noting that adding the MLE component allows Eskander's CEC to recover various types of errors that were not modeled previously.

the contribution of MLE is limited to words that are in the training data.


Given P (q 0 |q 1 ) defined, to correct the word q 1 we could iterate through all ever-observed words, and choose the one, that maximizes the posterior probability.

the practical considerations demand that we do not rank the whole list of words, but instead choose between a limited number of hypotheses h 1 , ..., h K : 2.


Patents often have a high concentration of scientific and technical terms that are rare in everyday language.

some scientific and technical terms usually appear with high frequency only in one specific patent.


terminologies also tend to be very sparse, either because they are related to the latest invention that has not made into everyday language, or because our limited patent dataset cannot possibly cover all possible technical topics.

these technical terms are also topical and they tend to have high relative frequency within a patent document even though they are sparse in the entire patent data set.


2010, where again all "unambiguous" models present superior performance compared to their "ambiguous" versions.

in this last work one of the dimensions of the tensors was kept empty (filled in with zeros).


Treating microblogs as standard texts and directly classifying them cannot achieve the goal of effective classification because of sparseness problem.

news on the Internet is of information abundance and many microblogs are news-related.


Furthermore, recall of individual words irrespective of their order approached and even exceeded that of a trained expert stenographer with seven workers contributing, suggesting that the information is present to meet the performance of a stenographer (Lasecki et al., 2012).

aligning these individual words in the correct sequential order remains a challenging problem.


However, they applied it only to the words with high frequencies in the documents (Fukumoto et al., 2013).

we applied it to the topic candidates obtained by LDA.


The top site was "HybH-Sum" by (Celikylmaz and Hakkani-Tur, 2010).

the method is a semi-supervised technique that needs a tagged training data.


First, consider the plural nominative word form kissat (cats) where the plural number is denoted by the 1-suffix -t. Then, by employing the features (2), the suffix -t is associated solely with the compound label NOMINATIVE+PLURAL.

by incorporating the expanded feature set (5), -t will also be associated to the sub-label PLURAL.


With different number of induced and gold standard clusters the 1-1 measure suffers because some induced clusters cannot be mapped to gold clusters or vice versa.

almost half the gold standard clusters in MTE contain just a few words and we do not expect our model to be able to learn them anyway, so the 1-1 measure is still useful for telling us how well the model learns the bigger and more distinguishable classes.


The success of auto-suggest depends upon showing users options they can recognize.

we know of no prior work on how to display grammatical relations so that they can be easily recognized.


First, because Chinglish and Chinese are written with the same characters, they render the same inventory of 416 distinct syllables.

the distribution of Chinglish syllables differs a great deal from Chinese (Table 2).


The context vocabulary C is thus identical to the word vocabulary W .

this restriction is not required by the model; contexts need not correspond to words, and the number of context-types can be substantially larger than the number of word-types.


This pattern takes the copular be as it appears in the source text.

most patterns use the lexical form of the main verb along with the appropriate form of the auxiliary do (do, does, did), for the subject-auxiliary inversion required in forming interrogatives.


Not having coreference resolution leads to vague questions, some of which can be filtered as discussed previously.

further work on filters is needed to avoid questions such as: Source sentence: Air cools when it comes into contact with a cold surface or when it rises.


Consequently, they get more replies to their messages (ReplyRate).

considering messages where the other person of the pair is addressed in the To list (ReplyRate-WithinPair), subordinates get more replies.


The AA problem with limited training data was attempted in (Stamatatos, 2007;Luyckx and Daelemans, 2008).

neither of them used a semisupervised approach to augment the training set with additional documents.


Second, CNG+SVM uses two learning methods on a single character n-gram view.

besides the character n-gram view, we also make use of the lexical and syntactic views.


provide support for a particular argument, acknowledge previous work that uses the same methodology, or exemplify work that would benefit from the outcomes of the author's work.

our current paper has more modest aims: we present initial results using existing IR-based approaches and we introduce an evaluation method and metric.


Crowdsourcing services such as Amazon's Mechanical Turk has since been successfully used for various annotation tasks in NLP (Jha et al., 2010;Callison-Burch and Dredze, 2010).

most applications of crowdsourcing in NLP have been concerned with classification problems, such as document classification and constructing lexica (Callison-Burch and Dredze, 2010).


Note that in MV we trust all annotators to the same degree.

crowdsourcing attracts people with different mo-tives, and not all of them are equally reliableeven the ones with Bronze level.


More importantly, however, POS tagging accuracy using crowdsourced annotations are on average only 2.6% worse than gold using professional annotations.

performance is much better than the weakly supervised approach by Li et al.


If true, this fact would have striking implications for theories and models of language acquisition, as well as numerous applications in natural language processing.

empirical investigations to date have focused on a small number of verbs.


In principle, this could be an unpredictable fact about the verb that must be acquired, much like the phonological form of the verb.

most theorists posit that there is a systematic relationship between the semantics of a verb and the syntactic frames in which it can appear (Levin and Hovav, 2005).


A first step is to be able to distinguish between strong and weak statements.

even this problem is understudied, partly due to a lack of data.


the apparent difficulty of the task, we found that many labels for the 386 pairs were reasonable.

in some cases, the labels were counterintuitive.


The task of identifying CD in text and referent CAs bears some similarity to coreference resolution.

coreference resolvers tried by the authors (namely CoreNLP (Recasens et al., 2013), ArkRef (O'Connor and Heilman, 2013) and the work of Roth and Bengston (2008)) were ineffective at this task.


Our results are further distinct from prior work by focusing on the communicative capacities of a variety of referents represented in documents.

the present focus upon determinerestablished phrases is more exclusive, and our results do not include demarcation of referents.


To be specific, the seed words list contains 8 to 12 emotional words for each of the six emotion categories.

3 it is important to note that the proposed models are flexible and do not need to have seeds for every topic.


To date, most of the work presented on deception detection has focused on the identification of deceit clues within a specific language, where English is the most commonly studied language.

a large portion of the written communication (e.g., e-mail, chats, forums, blogs, social networks) occurs not only between speakers of English, but also between speakers from other cultural backgrounds, which poses important questions regarding the applicability of existing deception tools.


This improvement is practically useful in the large-data setting and is also scientifically interesting in that it recovers some of the cognitive plausibility which originally motivated Börschinger and Johnson (2012).

in experiments on the dataset studied by Canini et al.


As such, at around k=12, the precision@k of most of the systems has almost reached the final precision.

even at k = 5, which only counts correct an answer in the top 5 human suggested results, our system still achieved a precision of around 67%.


According to our observation, the POS of a task topic is usually a proper noun ( ) and the POS of a task event is usually a transitive verb ( ) + common noun ( ) or an intransitive verb ( ).

a task topic may be the most important term in related search sessions .


This is similar to moving from a first to a second order HMM.

to the original model, we also distinguish between unknown entities in the first and second argument position.


Hashing has recently emerged to be a popular solution to tackling fast NNS, and been successfully applied to a variety of non-NLP problems such as visual object detection (Dean et al., 2013) and recognition (Torralba et al., 2008a;Torralba et al., 2008b), large-scale image retrieval (Kulis and Grauman, 2012;Gong et al., 2013), and large-scale machine learning (Weiss et al., 2008;Liu et al., 2011;Liu, 2012).

hashing has received limited attention in the NLP field to the date.


Given a query document vector q, we use the Cosine similarity measure to evaluate the similarity between q and a document x in a dataset: Then the traditional document retrieval method exhaustively scans all documents in the dataset and returns the most similar ones.

such a brute-force search does not scale to massive datasets since the search time complexity for each query is O(n); additionally, the computational cost spent on Cosine similarity calculation is also nontrivial.


Both annotators chose VAGUE to label ordered and said because the order is unclear.

they disagreed on evacuation with monitor.


Table 5 shows our set of instance-based syntactic and semantic features.

to the above described type-based features, these features do not rely on a background corpus, but are extracted from the clause being classified.


Although combinations of tokens could also be replaced by wild cards in any automatically acquired pattern, this would generally lead to an exponentially growing feature space.

the set of discourse markers in our work is fixed: for English, we use 61 markers annotated in the Penn Discourse TreeBank 2.0 (Prasad et al., 2008); for German, we use 155 one-word translations of the English markers, as obtained from an online dictionary.


We experimented with different vector values (absolute frequency, log frequency, pointwise mutual information (PMI)), distance measures (cosine, euclidean) and normalization schemes.

to S&K, who did not observe any improvements using PMI, we found it to perform best, combined with euclidean distance and no additional normalization.


In conclusion, the majority of Lesk variants focused on extending the gloss to increase the chance of overlapping, while the proposed NBM aims to make better use of the limited lexical knowledge available.

to string matching, the probabilistic nature of our model offers a "softer" measurement of gloss-context association, resulting in a novel approach to unsupervised WSD with state-of-the-art performance in more than one WSD benchmark (Section 4).


This will look very similar to structured dropout: the matrix E[P] is identical, and E[Q] has off-diagonal elements which are scaled by (1 − p) 2 , which goes to zero as K is large.

by including these elements, standard dropout is considerably slower, as we show in our experiments.


The various noising approaches for mDA give very similar results.

structured dropout is orders of magnitude faster than the alternatives, as shown in Table 3.


When translating dialogue, the length of each utterance will usually be short, so the system can simply start the translation process when it detects the end of an utterance.

in the case of lectures, for example, there is often no obvious boundary between utterances.


it becomes an object marker if the predicate has a potential voice type, which is usually translated into can, be able to, want to, or would like to.


However, the inflectional suffix of Spanish verbs include a hint of the person of the subject.

inferring Japanese subjects is more difficult than Spanish, since Japanese verbs usually do not have any grammatical cues to tell the subject type.


There are several ways to generate a potential voice in Japanese, but we usually put the suffix word (reru) or (rareru) after predicates.

these suffix words are also used for a passive voice.


In both the manual and automatic identification of sentence skeleton (rows 2 and 4), there is a significant improvement on the "All" data set.

using different skeleton identification results for training and inference (row 3) does not show big improvements due to the data inconsistency problem.


(2011) regard skeleton as a shortened sentence after removing some of the function words for better word deletion.

we define sentence skeleton as the key segments of a sentence and develop a new MT approach based on this information.


Related work in literature has proven that the expanded corpora can substantially improve the performance of ma-chine translation (Duh et al., 2010;.

the methods are still far from satisfactory for real application for the following reasons:  There isn't ready-made domain-specific parallel bitext.


As presented in subsection 3.2, the method combines translation model and language model to rank the sentence pairs in the general-domain corpus.

it does not evaluate the inverse translation probability of sentence pair and the probability of target language sentence.


Wikipedia has many language versions, and articles in one language contain hyperlinks to corresponding pages in other languages.

the coverage of different language versions of Wikipedia is very inconsistent.


These works were able to exploit the link structure and metadata common to all Wikipedia language versions.

when linking between different online encyclopedia platforms this is more difficult as many of these structural features are different or not shared.


Title translation is an effective and widely used method of creating cross-language links between encyclopedia articles.

(Wang et al., 2012; Adafre and de Rijke, 2005) title translation alone is not always sufficient.


In many instances, scene-based image descriptors provide enough information to generate a complete description of the image, or at least a sufficiently good one.

there are some kinds of images for which scene-based features alone are insufficient.


This is because these two edits would combine together as a word-order change.

in Figure 5b, if one edit includes a substitution between words with the same POS's, then it is likely fixing a word choice error by itself.


Intuitively, a linear combination of documentlevel language models can be used to incorporate content written by friends.

it should be noticed that some documents are more relevant than others, and should be weighted higher.


In this example, the topic certainly relates to a student protest as revealed by the top 3 terms which can be used as a good label for this topic.

previous work has shown that top terms are not enough for interpreting the coherent meaning of a topic (Mei et al., 2007).


Most previous topic labelling approaches focus on topics derived from well formatted and static documents.

in contrast to this type of content, the labelling of topics derived from tweets presents different challenges.


One might expect that the SUBST(t) edit operation that reads s = x i+1 and writes t = y j+1 would correspond to an arc with s, t as its input and output labels.

we give a more efficient design where in the course of reaching q C , the PFST has already read s and indeed the entire right input context C 2 = x i:(i+N 2 ) .


It is outperformed by the best supervised approach in two domains, NEWS and PUBMED, using the nDCG-3 and nDCG-5 metrics.

the best label proposed by our methods is judged to be better (as shown by the nDCG-1 and Top-1 Av.


ZPar 4 (Zhang and Clark, 2008; Zhang and Nivre, 2011) performs transition-based dependency parsing with a stack of partial analysis and a queue of remaining inputs.

to MaltParser (local model and greedy deterministic search) ZPar applies global discriminative learning and beam search.


In machine translation, multiple beams are used to prune translation hypotheses at different levels of granularity (Zens and Ney, 2008).

the focus is improving the speed of translation decoder rather than improving translation quality through enforcement of hypothesis diversity.


For example, (3a) in Figure 3 is transformed into (6a)  Table 2: Token count and data split for PPCMBE in Figure 5, and likewise (4a) is transformed into (6b).

(4b) remains as it is, because the following CP in that case is a complement, as indicated by the THT function tag.


We address both tasks together with the regexes.

to the sort of head rules in (Collins, 1999), these refer as little as possible to specific POS tags.


(such as in document-level topic models).

language shows observable priming effects, sometimes called triggers, where the occurrence of a given term decreases the surprisal of some other term later in the same discourse (Lau et al., 1993;Church and Gale, 1995;Beeferman et al., 1997;Church, 2000).


Also, TwiCal uses G 2 test to choose an entity y with the strongest association with a date d to form a binary tuple y, d to represent an event.

the structured representation of events can be directly extracted from the output of our LEM model.


It is not easy to accurately identify named entities in the Twitter data since tweets contain a lot of misspellings and abbreviations.

it is often observed that events mentioned in tweets are also reported in news articles in the same period (Petrovic et al., 2013).


This result is promising because it demonstrates that the system generated morphs contain new and unique characteristics which are unknown to the decoder.

from Figure 2 we can see that system generated morphs can be more easily resolved into the right target entities than human generated ones which are more implicit.


Pairs in the bottomleft region of the CONC-SUBJ space (objective adjectives with abstract nouns, such as green politics) seem to exhibit a non-literal, or at least non prototypical modification type.

for pairs in the objective+concrete corner, the adjectives appear to perform a classifying or categorizing function (baptist minister).


While the results reported so far on that annotation task were relatively low, we suggest that the task itself may be more complicated than what is actually required in textual inference scenarios.

the results obtained for our task, which does fit textual inference scenarios, are promising, and encourage utilizing algorithms for this task in actual inference systems.


One type of such cases are non-core arguments, which cannot be Definite NIs.

textual inference deals with non-core arguments as well (see example 3 in Table 1).


This binary distinction assumes a clear boundary between the two; in other words, it assumes that metaphoricity is a discrete property.

three strands of theoretical research show that metaphoricity is not a discrete property.


This is a result of not having perfect agreement across all participants.

in spite of this, the measure makes a good distinction between utterances.


8, whose complexity is the same as the monolingual case.

the complexity of calculating the transition probability, in Eqs.


(Ravi, 2013) reports the most efficient method so far: It only consumes about 3h of computation time.

as mentioned before, those results are not directly comparable to our work, since they use additional context information on the target side.


Most MT system combination work uses MT systems employing different techniques to train on the same data.

in this paper, we use the same MT algorithms for training, tuning, and testing, but vary the training data, specifically in terms of the degree of source language dialectness.


Comparing the two derivations, (a) is more reasonable and yields a better translation.

(b) wrongly translates phrase " † â9" to "and Sharon" and combines it with [ÙŸ;Bush] incorrectly, leading to a bad translation.


Furthermore, in phrase-based MT, most decodable sentences are very short, while in HIERO the lengths of decodable sentences are more evenly distributed.

in the following experiments, due to efficiency considerations, we use the "tight" rule extraction in cdec that is more strict than the standard "loose" rule extraction, which generates a reduced rule set and, thus, a reduced reachability.


Take valency features for example, previous work (Zhang and Nivre, 2011) has shown that such features are important to parsing accuracy, e.g., it may inform the parser that a verb already has two objects attached to it.

such information might be inaccurate when the verb's modifiers contain punctuations.


Accuracy degrades somewhat from the SU-PERVISED initialization, since the data likelihood objective differs from the objective of maximizing tagging accuracy.

the final SUPERVISED performance of 94.1% shows that there is substantial room for improvement over the UNIFORM initializer.


The SUPERVISED TRANSITIONS initialization is estimated from observations of consecutive tags in a labeled corpus.

our oBSERVATIoNAL initializer is likewise estimated from the relative frequency of consecutive tags, taking advantage of the structure of the tag dictionary D. it does not require a labeled corpus.


For instance, Toutanova and Galley (2011)  parameters for IBM Model 1 are not unique, and alignments predicted from different optimal parameters vary significantly in accuracy.

the effectiveness of observational initialization is somewhat surprising because EM training includes these unambiguous tag pairs in its expected counts, even with uniform initialization.


This paper has demonstrated a simple and effective learning method for type-supervised, transductive part-of-speech tagging.

it is an open question whether the technique is as effective for tag dictionaries derived from more natural sources than the labels of an existing treebank.


algorithm quantifies the concreteness of concepts that lack such a rating based on their proximity to rated concepts in a semantic vector space.

to each of these approaches, the image dispersion approach requires no hand-coded resources.


For example, for a relation pay an official visit to, with a statement (Bush, pay an official visit to, China), an entity pair (Bush, China) is in the "support set", which is a set of co-occurring entity pairs of pay an official visit to.

when its support set is {(Bush, China), (Mandelson, Moscow), (Rice, Israel)}, and that of visit is {(Bush, China), (Rice, Israel), (Medvedev, Cuba)}, we can infer their semantic equivalence based on the set intersection: we propose to explore corpus latent features (LF), to complement the sparsity problem of EF: Out of 158 randomly chosen correct relation translation pairs we labeled, 64% has only one co-occurring entity pair, which makes EF not very effective to identify these relation translations.


That is, we always 讨论 (discuss) before we 批 准 (ratify) something and hence the temporal behavior of 讨论 (discuss) is also very similar to that of ratify.

it can be correctly translated using EF.


Mining opinions from text by identifying their positive and negative polarities is an important task and supervised learning methods have been quite successful.

supervised methods require labeled samples for modeling and the lack of sufficient training data is the performance bottle-neck in opinion analysis especially for resource scarce languages.


This makes learning more difficult and slows the speed of parameter updates by a factor of two.

given that our post-processing step is concerned only with the alignments of the unknown words, so it is more sensible to only annotate the unknown words.


While (Sutskever et al., 2014) produces better translations for sentences with frequent words (the left part of the graph), they are worse than best 7 Their unknown replacement method and ours both track the locations of target unknown words and use a word dictionary to post-process the translation.

the mechanism used to achieve the "tracking" behavior is different.


As the first effort to apply attention model to machine translation, it sends the state of a decoding RNN as attentional signal to the source end to obtain a weighted sum of embedding of source words as the summary of relevant context.

inCNN uses 1) a different attention signal extracted from proceeding words in partial translations, and 2) more importantly, a convolutional architecture and therefore a highly nonlinear way to retrieve and summarize the relevant information in source.


For example, identifying the gender of a person is important for generating a good description.

object recognizers are not (yet) able to reliably achieve this distinction, and we only have a single recogniser for "persons".


This could be attributed to the shift in the types of scenes depicted in each data set.

transferring VDR from the VLT2K to the Pascal1K data set improves the generated descriptions from 7.4 → 8.2 Meteor points.


Prior work has generated sentences that describe 2D images (Farhadi et al., 2010;Kulkarni et al., 2011;Karpathy et al., 2014) and referring expressions for specific objects in images (FitzGerald et al., 2013;Kazemzadeh et al., 2014).

generating scenes is currently out of reach for purely image-based approaches.


We see that for the simple seed sentences both the rule-based and combined model approach the quality of human-created scenes.

all methods have significantly lower ratings for the more complex MTurk sentences.


Thus, we expect that our method for lexical grounding can facilitate development of text-to-scene systems in other languages.

additional data collection and experiments are necessary to confirm this and identify challenges specific to other languages.


Instead of computing the similarity directly as we do for DIRECTSIM, we first transform the representation of the g-phrase in one TEXTCHUNK using a transformation matrix M ∈ R d×d , then compute the match score by inner product and sigmoid activation: Our motivation is that for a TEXTCHUNK relation like clause coherence, the two TEXTCHUNKS need not have any direct similarity.

if we map the representations of TEXTCHUNK S 1 into an appropriate space then we can hope that similarity between these transformed representations of S 1 and the representations of TEXTCHUNK S 2 do yield useful features.


Titov and McDonald (2008) emphasize the importance of an unsupervised approach for aspect detection.

the authors also indicate that standard LDA (Blei et al., 2003) methods capture global topics and not necessarily pertinent aspects -a challenge that we address in this work.


We found that in 63% of these missed predictions, Seed-edLDA predicts LECTURE-CONTENT, instead of QUIZ-CONTENT, and vice versa.

pSL-Joint uses both coarse and fine SeededLDA scores and captures the dependency between a coarse aspect and its corresponding fine aspect.


The only requirement is that the learned embeddings should be compatible within each individual fact.

they fail to discover the intrinsic geometric structure of the embedding space.


With enough GRE data, the prediction error may be backpropagated to directly adjust or learn embedding in the look-up tables.

given the limited size of the GRE data, we only employed the top hidden layers to non-linearly merge the distances between a word pair that are obtained within each of the modules in the Contrast Inference Layer.


Without the sampling, the F-score achieved on the test data is 83%.

the findings presented above should not be simply taken as that distributional hypothesis is not useful for learning lexical contrast.


Our regularizer framework is able to propagate weights from the conjunctive features seen in training to unseen conjunctive features that are close to each other in the projected space (these are the yellow and red cells in the matrix).

1 and 2 regularization techniques can not put weight on unseen conjunctions.


Most similarly to our work, Weiss and Taskar (2013) improve performance for several structured vision tasks by dynamically selecting features at runtime.

they use a reinforcement learning approach whose computational tradeoffs are better suited to vision problems with expensive features.


Searn (Daumé III et al., 2009) and DAgger (Ross et al., 2011) are two popular principled frameworks for reducing sequential prediction to classification by learning a classifier on additional synthetic training data.

as we do in our experiments, practitioners often see good results by training on the gold standard labels with an off-the-shelf classification algorithm, as though classifying IID data (Bengtson and Roth, 2008;Choi and Palmer, 2012).


(These two requirements are both incorporated into our novel parameter estimation algorithm.)

other work (Weiss and Taskar, 2013;He et al., 2013) learns a separate classifier to determine when to add features.


(2012) greatly increase available raw material for paths by augmenting KB-schema relations with relations defined by the text connecting mentions of entities in a large corpus (also known as OpenIE relations (Banko et al., 2007)).

these symbolic methods can produce many millions of distinct paths, each of which is categorically distinct, treated by PRA as a dis-tinct feature.


We introduce a word-representation model to capture meaningful semantic regularities for words and adopt a framework based on a convolutional neural network (CNN) to capture sentence-level clues.

cNN can only capture the most important information in a sentence and may miss valuable facts when considering multiple-event sentences.


In S1, beats is a trigger of type Elect.

in S2, beats is a trigger of type Attack, which is more common than type Elect.


e i is another embedding for e i , see Morin and Bengio (2005)  The CNN, with max-pooling layers, is a good choice to capture the semantics of long-distance words within a sentence (Collobert et al., 2011).

as noted in the section 1, traditional CNN is incapable of addressing the event extraction problem.


To extract the most important features (max value) within each feature map, traditional CNNs (Collobert et al., 2011;Kim, 2014;Zeng et al., 2014) take one feature map as a pool and only get one max value for each feature map.

single max-pooling is not sufficient for event extraction.


(2013) as the traditional method, which uses traditional lexical features, such as n-grams, POS tags and some entity information.

we only use word embedding as our lexical feature.


The RPI BLENDER KBP system (Yu et al., 2014) casts SFV in this framework, using a graph propagation method that modeled the credibility of systems, sources, and response values.

they only report scores on the 2013 SFV data which contain less complicated and easier queries compared to the 2014 data.


Another direction is to use more complex translation models such as hierarchical models (Chiang, 2007).

these approaches suffer from the long-distance reordering issue and computational complexity.


Statistical syntactic parsers are usually trained on tree-annotated corpora.

corpora annotated with BTG parse trees are unavailable, and only the gold standard permutation y is available.


Experimental results suggest the effectiveness of online learning as a way to exploit user feedback to tailor QE predictions to their quality standards and to cope with the heterogeneity of data coming from different domains.

though robust to user and domain changes, the method is solely driven by the distance computed between predicted and true labels, and it does not exploit any notion of similarity between tasks (e.g.


The methods in these two strands have shown their effectiveness on lexical selection.

correlations between sentence-and document-level contexts have never been explored before.


The politics topic can be further used to enable the decoder to select a correct translation "issue" for another source word "K /wèntǐ", which is consistent with this topic.

if we know that this document mainly focuses on the politics topic, the candiate translation "stance" will be more compatible with the context of "á|/lìchǎng" than the candiate translation "attitude".


QA has had a long history of using pipeline models that extract a limited number of high-level features from induced representations of questionanswer pairs, and then built a classifier using some labelled corpora.

we learnt these structures and performed machine comprehension jointly through a unified max-margin framework.


Previous work in the literature has demonstrated the effectiveness of the category information for question retrieval (Cao et al., 2010;Zhou et al., 2013).

we argue that the category information benefits the word embedding learning in this work.


(2014) used convolutional neural networks to answer single-relation questions on REVERB (Fader et al., 2011).

the system worked on relation-entity triples instead of more structured knowledge bases.


Its correct response should contain only one entity (vivian liberto).

our system addi-tionally outputs june carter cash who is his second wife, because both the candidate answers are connected to johnny cash by the relation people.person.spouse s. In order to solve this issue, we need to define some ad-hoc operators used for comparisons or develop more advanced semantic representations.


The first row of Table 5 confirms that the hubs found in the neighbourhoods of ridgemapped query terms are items that tend to be closer to the search space mean vector, and that this effect is radically reduced with max-margin estimation.

the second row of the table shows another factor at play, that has a major role in the cross-modal setting, and it is only partially addressed by max-margin estimation: Namely, in vision-to-language mapping, there is a strong tendency for hubs (that, recall, have an important effect on performance, as they enter many nearest neighbour lists) to be close to a training data point.